{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37886e17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fb44298",
   "metadata": {},
   "outputs": [],
   "source": [
    "def constplot(cs,fs,nbins=50,ax=None,aspect=10,cmap='jet',target=None):\n",
    "    if ax==None:\n",
    "        f, ax = plt.subplots()\n",
    "    bcs =np.linspace(0,1,nbins)\n",
    "    bfs =np.linspace(-12,0,nbins)\n",
    "    o,a,b = np.histogram2d(fs,cs,bins=[bfs,bcs])#,range=((-1,1),(-1,1)))\n",
    "    #o,a,b = np.histogram2d(cs,fs,bins=[bcs,bfs])#,range=((-1,1),(-1,1)))\n",
    "    aaa=ax.imshow(10*np.log10(o[:,::-1].T)\n",
    "              ,extent=(-12,0,0,1.0)\n",
    "              ,aspect=aspect\n",
    "              ,cmap=cmap)\n",
    "    colorbar(aaa,ax=ax)\n",
    "    return o,bfs,bcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1410a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder='./save/embed/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5630b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cs_fs_for_object_list_bilingual_train_log_3_tags_epoch45_ENGE\n",
      "dict_keys(['car', 'train', 'airplane', 'boat', 'television', 'clock', 'phone', 'camera', 'dog', 'cow', 'tree', 'mountain'])\n"
     ]
    }
   ],
   "source": [
    "'''train'''\n",
    "\n",
    "filename0='cs_fs_for_object_list_bilingual_train_log_3_tags_epoch45'\n",
    "filename0='cs_fs_for_object_list_bilingual_train_log_3_tags_epoch45_ENGE'\n",
    "\n",
    "print(filename0)\n",
    "data=np.load(folder+filename0+'.npz',allow_pickle=True)\n",
    "cs_collect = data['cs_collect'][()]\n",
    "fs_collect = data['fs_collect'][()]\n",
    "\n",
    "print(cs_collect.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af2b956",
   "metadata": {},
   "source": [
    "1) Plot Sim_Contextual vs F_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe48d3be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 ['car', 'train', 'airplane', 'boat', 'television', 'clock', 'phone', 'camera', 'dog', 'cow', 'tree', 'mountain']\n",
      "12 ['dog', 'cow', 'duck', 'eagle', 'corn', 'stone', 'leaf', 'magazine', 'car', 'train', 'wine', 'hair']\n"
     ]
    }
   ],
   "source": [
    "#if object_file == \"object_list_bilingual_train\":\n",
    "from data.PWI.object_list_bilingual import food_list,animal_list,food_list_cate,pseudo_list,hypernymy_list \n",
    "\n",
    "print(len(food_list),food_list)\n",
    "print(len(animal_list),animal_list)\n",
    "Nscan = len(food_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b0d6fb2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hund', 'kuh', 'ente', 'adler', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n"
     ]
    }
   ],
   "source": [
    "print(pseudo_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6d2a0123",
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_same_all=[]\n",
    "cs_diff_all=[]\n",
    "cs_pseudo_all=[]\n",
    "cs_hypernymy_all=[]\n",
    "fs_same_all=[]\n",
    "fs_diff_all=[]\n",
    "fs_pseudo_all=[]\n",
    "fs_hypernymy_all=[]\n",
    "for food_idid in range(0,Nscan):    \n",
    "    food_list2=[food_list[food_idid]]\n",
    "    target_object=food_list2[0]\n",
    "    cs_same_all+=cs_collect[target_object]['same']\n",
    "    cs_diff_all+=cs_collect[target_object]['diff']\n",
    "    cs_pseudo_all+=cs_collect[target_object]['pseudo']\n",
    "    cs_hypernymy_all+=cs_collect[target_object]['hypernymy']\n",
    "    fs_same_all+=list(fs_collect[target_object]['same']*-1)\n",
    "    fs_diff_all+=list(fs_collect[target_object]['diff']*-1)\n",
    "    fs_pseudo_all+=list(fs_collect[target_object]['pseudo']*-1)\n",
    "    fs_hypernymy_all+=list(fs_collect[target_object]['hypernymy']*-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0b90808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'car' (311),'train' (282),'airplane' (14),'boat' (349),'television' (20),'clock' (307),'phone' (475),'camera' (21),'dog' (963),'cow' (535),'tree' (286),'mountain' (47),"
     ]
    }
   ],
   "source": [
    "for key in cs_collect.keys():\n",
    "    print(\"'\"+str(key)+\"'\",\"(\"+str(len(cs_collect[key]['diff']))+\"),\",end='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5287756e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def constplot(cs,fs,nbins=50,ax=None,aspect=10,cmap='twilight_shifted',target=None):\n",
    "    if ax==None:\n",
    "        f, ax = plt.subplots()\n",
    "    bcs =np.linspace(0,1,nbins)\n",
    "    #bfs =np.linspace(-12,0,nbins)\n",
    "    bfs =np.linspace(0,14,nbins)\n",
    "    #o,a,b = np.histogram2d(fs,cs,bins=[bfs,bcs])#,range=((-1,1),(-1,1)))\n",
    "    o,a,b = np.histogram2d(cs,fs,bins=[bcs,bfs])#,range=((-1,1),(-1,1)))\n",
    "    im=ax.imshow(10*np.log10(o[:,::-1].T)\n",
    "              ,extent=(0,1.0,0,12)\n",
    "              ,aspect=aspect\n",
    "              ,cmap=cmap\n",
    "                ,vmin=0, vmax=45)\n",
    "    #colorbar(aaa,ax=ax)\n",
    "    return o,bfs,bcs,im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "af5d0bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./save/figures/plot_cs_fs_for_object_list_bilingual_train_log_3_tags_epoch45_ENGE.svg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lab\\anaconda3\\envs\\visualbert\\lib\\site-packages\\ipykernel_launcher.py:9: RuntimeWarning: divide by zero encountered in log10\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "nbins=25\n",
    "aspect=8\n",
    "fs=16\n",
    "xa,xb=0,14\n",
    "\n",
    "\n",
    "matplotlib.rc('xtick', labelsize=fs) \n",
    "matplotlib.rc('ytick', labelsize=fs) \n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 3,figsize=[12,3],sharey=True)\n",
    "\n",
    "#o1,bfs,bcs,im=constplot(cs_same_all[:len(fs_same_all)],fs_same_all,nbins,ax[0],aspect);\n",
    "o1,bfs,bcs,im=constplot(cs_diff_all,fs_diff_all,nbins,ax[0],aspect);\n",
    "o2,bfs,bcs,im=constplot(cs_pseudo_all,fs_pseudo_all,nbins,ax[1],aspect);\n",
    "o3,bfs,bcs,im=constplot(cs_hypernymy_all,fs_hypernymy_all,nbins,ax[2],aspect);\n",
    "\n",
    "\n",
    "ax[0].set_aspect('auto')\n",
    "ax[0].set_ylim([xa,xb])\n",
    "#ax[0].set_xlim([0.0,1.05])\n",
    "ax[0].set_xlabel('Cosine similarity',fontsize=fs)\n",
    "ax[0].xaxis.set_label_coords(1.7,-0.18)\n",
    "ax[0].set_ylabel('F-score',fontsize=fs)\n",
    "ax[0].set_title(r' $\\bf T_{diff-EN}$',fontsize=fs)\n",
    "\n",
    "ax[0].grid()\n",
    "#ax[1].set_title('diff category \\n %s to %s'%(answer_item,animal_list[ans_pos]))\n",
    "ax[1].set_aspect('auto')\n",
    "ax[1].set_ylim([xa,xb])\n",
    "ax[1].set_title(r' $\\bf T_{diff-DE}$',fontsize=fs)\n",
    "ax[1].grid()\n",
    "#ax[1].set_xlabel('Cosine similarity',fontsize=fs)\n",
    "\n",
    "#ax[2].set_title('pseudo \\n %s to %s'%(answer_item,pseudo_list[ans_pos]))\n",
    "ax[2].set_aspect('auto')\n",
    "ax[2].set_ylim([xa,xb])\n",
    "ax[2].set_title(r' $\\bf T_{DE}$',fontsize=fs)\n",
    "ax[2].grid()\n",
    "#ax[2].set_xlabel('Cosine similarity',fontsize=fs)\n",
    "\n",
    "# #ax[3].set_title('hypernymy \\n %s to %s'%(answer_item,hypernymy_list[ans_pos]))\n",
    "# ax[3].set_aspect('auto')\n",
    "# ax[3].set_ylim([xa,xb])\n",
    "# ax[3].grid()\n",
    "\n",
    "fig.colorbar(im, ax=ax.ravel().tolist())\n",
    "\n",
    "folderfigure='./save/figures/'\n",
    "figuresave=folderfigure+'plot_{}.svg'.format(filename0)\n",
    "print(figuresave)\n",
    "#savefig(figuresave)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6cfb40",
   "metadata": {},
   "source": [
    "2) Gaussian Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "68bbb2a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def gauss(x, *p):\n",
    "    A, mu, sigma = p\n",
    "    return A*numpy.exp(-(x-mu)**2/(2.*sigma**2))\n",
    "\n",
    "#data = numpy.random.normal(size=10000)+10\n",
    "fit_mean=[]\n",
    "for i in range(0,o1.shape[0]-0):\n",
    "    data=((o1+o2).T)[i]\n",
    "    data=data[data!= float('-inf')]\n",
    "    #hist, bin_edges = numpy.histogram(data, density=True)\n",
    "    bin_centres = (bcs[:-1] + bcs[1:])/2\n",
    "    # p0 is the initial guess for the fitting coefficients (A, mu and sigma above)\n",
    "    p0 = [0.2, 0., 1.]\n",
    "\n",
    "    coeff, var_matrix = curve_fit(gauss, bin_centres, data, p0=p0,maxfev =5000)\n",
    "\n",
    "    hist_fit = gauss(bin_centres, *coeff)\n",
    "    fit_mean.append(coeff[1])\n",
    "    plot(bin_centres, data, label='Test data')\n",
    "    plot(bin_centres, hist_fit, label='Fitted data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "770f4d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'F-score')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(bfs[:-1],fit_mean,'^')\n",
    "ylim([0,1])\n",
    "xlim([-0.5,10])\n",
    "ylabel('Cosine similarity',fontsize=fs)\n",
    "xlabel('F-score',fontsize=fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "873875d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "-0.02447 x + 0.7377\n",
      " \n",
      "-0.003764 x + 0.1513\n"
     ]
    }
   ],
   "source": [
    "filename0='./save/embed/category_gaussian_fit_all_bilingual.npz'\n",
    "data=np.load(filename0)\n",
    "ENGE_0th_all=data['ENGE_0th_all']\n",
    "EN_0th_all=data['EN_0th_all']\n",
    "ENGE_12th_all=data['ENGE_12th_all']\n",
    "EN_12th_all=data['EN_12th_all']\n",
    "bfs=data['bfs']\n",
    "\n",
    "filename0='./save/embed/category_gaussian_fit_all_bilingual_GEVQA.npz'\n",
    "data=np.load(filename0)\n",
    "GE_0th_all=data['GE_0th_all']\n",
    "GE_12th_all=data['GE_12th_all']\n",
    "\n",
    "xend=15\n",
    "ENGE_0th=ENGE_0th_all[:xend]\n",
    "EN_0th=EN_0th_all[:xend]\n",
    "ENGE_12th=ENGE_12th_all[:xend]\n",
    "EN_12th=EN_12th_all[:xend]\n",
    "\n",
    "GE_0th=GE_0th_all[:xend]\n",
    "GE_12th=GE_12th_all[:xend]\n",
    "\n",
    "x1=bfs[:xend]\n",
    "\n",
    "train1=(ENGE_12th+EN_12th+GE_12th)/3\n",
    "z = np.polyfit(x1, train1, 1)\n",
    "p1 = np.poly1d(z)\n",
    "#p(xp)\n",
    "print(p1)\n",
    "train0=(ENGE_0th+EN_0th+GE_0th)/3\n",
    "z = np.polyfit(x1, train0, 1)\n",
    "p0 = np.poly1d(z)\n",
    "#p(xp)\n",
    "print(p0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9572745d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[4,8])\n",
    "\n",
    "xp = np.linspace(0, 9.5, 10)\n",
    "\n",
    "ms=8\n",
    "fs=14\n",
    "plot(ENGE_12th,x1,'^',ms=ms,linestyle = 'None',markeredgecolor='r',markerfacecolor='None')\n",
    "plot(EN_12th,x1,'^',ms=ms,linestyle = 'None',markeredgecolor='b',markerfacecolor='None')\n",
    "plot(GE_12th,x1,'^',ms=ms,linestyle = 'None',markeredgecolor='c',markerfacecolor='None')\n",
    "plot(p1(xp),xp,'k')\n",
    "\n",
    "plot(ENGE_0th,x1,marker ='o',ms=ms,linestyle = 'None',markeredgecolor='r',markerfacecolor='None')\n",
    "plot(EN_0th,x1,marker ='o',ms=ms,linestyle = 'None',markeredgecolor='b',markerfacecolor='None')\n",
    "plot(GE_0th,x1,'o',ms=ms,linestyle = 'None',markeredgecolor='c',markerfacecolor='None')\n",
    "plot(p0(xp),xp,'k--')\n",
    "\n",
    "\n",
    "\n",
    "xlim([0,1.0])\n",
    "ylim([-0.5,10])\n",
    "\n",
    "legend([r'$VQA_{EN&DE}$ contextual',\n",
    "       '$VQA_{EN}$ contextual',   \n",
    "       '$VQA_{DE}$ contextual',        \n",
    "       'polyfit contextual',\n",
    "       '$VQA_{EN&DE}$ word',\n",
    "       '$VQA_{EN}$ word',\n",
    "       '$VQA_{DE}$ word',\n",
    "       'polyfit word'],\n",
    "       fontsize=fs-2,\n",
    "       ncol=2\n",
    "      )\n",
    "ylabel('F-score',fontsize=fs)\n",
    "xlabel('Cosine similarity',fontsize=fs)\n",
    "grid()\n",
    "matplotlib.rc('ytick', labelsize=fs) \n",
    "matplotlib.rc('xtick', labelsize=fs) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc335c0d",
   "metadata": {},
   "source": [
    "3) F_score vs F_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "daa267b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def constplot2(cs,fs,nbins=50,ax=None,aspect=10,cmap='twilight_shifted',target=None):\n",
    "\n",
    "    bcs =np.linspace(0,12,nbins)\n",
    "    bfs =np.linspace(0,12,nbins)\n",
    "    o,a,b = np.histogram2d(fs,cs,bins=[bfs,bcs])#,range=((-1,1),(-1,1)))\n",
    "    #o,a,b = np.histogram2d(cs,fs,bins=[bcs,bfs])#,range=((-1,1),(-1,1)))\n",
    "    im=imshow(10*np.log10(o[:,::-1].T)\n",
    "              ,extent=(0,10,0,10)\n",
    "              ,aspect=1\n",
    "              ,cmap=cmap)\n",
    "    grid()\n",
    "    return o,bfs,bcs,im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "93aa9bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./save/figures/plot_F2F_cs_fs_for_object_list_bilingual_train_log_3_tags_epoch45_ENGE.svg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lab\\anaconda3\\envs\\visualbert\\lib\\site-packages\\ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log10\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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UXZgxsz6f5tKfgTrnckNqTrX1gUujtdZekzRL0oQ0B6UugfbBHJ/OOVdCKH1Fd4ykB0rez4hWoajWRuBHhNUtVgC7E9Zdu0/SQWb2RG8H+/NK51x+pH8GutLMDqj3cmb2InBaSdLdku4grIJxARVGYHgAdb1KHtUTt/CxH8TSkqaf2/LQ/WJpj97x7VjaJKbG0pKmrnMDTf83tZjZs5LuAQ6stG+aALocX3PIOdcAORqxI1Jkp2IANbNxWeTGOecqyUMAjZYrOoTNa7H1yKvwzrlcMKAz4xAqaXL0z/2j16MkrQBWmNk8SZcReiMtIDQitQDnEWrd8WdLZTyAOudyI+sACswse3919DoPaCU0Fn0BmApsDawE5gAXRZMk9coDqHtL2lE9Sfvt+5mLYmlDRsb/e7Wvbo+lHfy1S2NpW454NTa6KamhykcdDSydlm0ANbNeW6XM7KfAT2s9vwdQ51wuGNYXJdA+5QHUOZcbWZdA+1rVo4sk7SvpN5JWRkOf9ovSvy0p3vHPOedSMKDDOlNteVFVAJV0KKG1anfgF2XHd9K9R79zzlWl0yzVlhfVVuH/FbgT+DjQDJQ+5X8ImJJNtlyt6mkIStrvhHHTY2lJI4zesdeIWNrrr8RnQPzxRUfE0pK0tbXxn3M/UZbq6xoNZGZGR2d+gmMa1QbQ/YBPmJlJKr/TlcDYbLLlnBuM8lQ9T6PaALoB2LKHz94JvFZfdpxzg5VB4Uqg1TYi3QOcqe4T8nXd8T8ROqA651zVzKDDLNWWF9WWQC8E7gUeBX5NCJ6flfTvhKFSFWcvcc65ZEZ75wCuwpvZo5I+BHyPMFeeCA1JdwMT0wx9cn2rngajJGO/ODGWtnZhfK2tvy1cH0vrWLgilnbC1dNjaTOXxdOS+PpHA1voxpSf0mUaVXekN7OHgA9LGg6MBlab2brMc+acG1TMBvAzUElbSPptVALFzDaY2Qv9FTwl3RGt3fzN/ri+cy5bhrGpszPVlhepA6iZvQkcXs0xfUXSicDe/Z0P51y2Ojot1ZYX1QbDe4F0azz0EUkjge8DZ/VnPpxz2RoMrfBfBW6RtAa4BXiRskmkG7AW83eBRWZ2o6Rf9PG16pJ1o0f5qKC0jS9JI4eS8rb3kefH0uz512NpzXvFx0skNRj959x0I4eSRjt96brWWJo3GA1sBrR35Cc4plFtAH0ser082spZDedMLRqLPwWvvjs3IOWpdJlGtcHuYvpp2RJJQwnrN/9bmu5SkqYB0wDGjh1LW1tb32YwwbQLp8bS6snHUV9tiZ1rzZo1Fc9ZfhzApM7tY2lbbpPw1W5KGHg2YmM8bc/4fmnvNSl/ae6riAbqfWVhwI+FN7PpfZSPNP4FGAF8K83OZjYDmAHQ0tJira2tfZezHpw5+dxYWj3V0KumTu/2fuayE2lra6PSvZUfB8lLBCdX4eOdLJKr8PGqfnwykPT5+9J1rRXvq4jSfF+DlQGbBnIABZD0TsKz0ImEfqCvAG3Av5vZS5nmbvM1dyJ03D8VGCZpWMnHw6KGpTfMLD79j3OuEEI/0P7ORXWqCqCS3kMYDz+S0CL/NLA98BVgiqQPmtlfs84ksAswHLg+4bOzo21f4JE+uHbN6iltJjXyxPe5g2kXTu1W0k265ojxu8bSWid/J1U+xr17TCzt3jPuiqWlbdBKknSsV3MHp84BXgL9DmHGpYPMbFlXoqSdgbuiz9PV26rzCDApIX0uIaheSwjmzrmCGgxV+EnAaaXBE8DMnpE0nc1LhmbKzFYTHhN0IwngGTOLfeacKxiDHA0ySqXaALoFEJ9JIngj+tw556pWxPlAqw2gjwCnS7q9tMO8QlHwizT4GWSlNZ+dc8VhZrQP8AB6MfA74AlJvySMRNoeOAHYDTg62+wNXmmmpXtk5Z9oa2vrtu+USUntbHEjR8ZH8W4/emgs7b7vxh8tJ3WB2mdMPM1HDrlqZVmFl7QjofvjAYTBNyOA8eWPICWNIkzR+fFonwXAP5vZY1RQ1Vh4M7sDOIZQXb8AuAr4OrAGOMbM4s2zzjmXQqjCp9tS2hX4FPAqYc7imKj2PAs4Ejgd+CQwFJgbBeBe1TIf6B3AHZK2BEYBr/p8oM65uhl0ZDsWfr6ZvQNA0qnARxP2OQ44FDjMzOZG+y4AlgJfA87o7QI1T01nZuvM7HkPns65LBihCp9mS3W+dBMbHQe80BU8o+NeA24Djq90cFUBVNK/SLqyh8+ukHRONedzzrkuZmE2pjRbhvYEHk9IXwTsJGmr3g6utgp/MnBZD589QhgR9L0qzzkgJI0c2mvCl2NpCx/7QSwtqbElaYq3NIZ96J2xtD33HxFLGz40/rfzySXxSULS5te5LFTRCD9G0gMl72dE819UazSwLCF9VfQ6itDGk6jaALoT0NNQzSXAzlWezznnAqtqKOdKMzsgg6uK5BnmUnWRrDaArgN26OGzHYGEec6cc64yAzoaPx3QKkIptNyo6PXV3g6uthHpbuCcstmQiN5/lR66CjjnXEWWbSNSSosIz0HL7QEsN7Meq+9QfQl0OnAf8JSk64HnCSXSk4BtgalVns8554BQArXGj0SaBZwsaaKZzQOQtA1wLFBxyaBqJ1R+VNIk4N8IPfybgE7CFHefNLNHq8z8gJa8JlC6dYKSlDdK/Wz2n9lm/Vp+NvvPb6Ud/MF4o+Fr6+L1ogf/tDaW9uOLPhxL2+cbteTUuRpY9lV4SZOjf+4fvR4laQWwIgqYswgjj66PehG9CpxHeAb63Urnr6Uj/f3AhySNYHNH+vXVnsc550p19QPN2Myy910zxs0DWs2sU9IxhELh1YR5hxcAk8zs2Uonr3kBuChoeuB0zmXDLPMqfJoJh8xsFXBKtFWl2hnpm4AmM2svSTsCeB8wx8werjYDzjnXpbNgi/JUWwK9kdBVaQqApNPYXCTeJOloM5udYf6cc4OEVdcPNBeqDaDvJzQedTkH+AmhC9MMwgxNhQ2gSaOJ+npdI4DdtjoylrbV8R+IpXUsXNHt/cZNRqeF19489WS8e2776vZYWtJUeD7qyDVSqtHrOVJtP9DtCF2XkLQrMB74gZm9AfwMmJBt9pxzg4aFKnyaLS+qLYG+TujvCdBKGE61MHrfQWjBcs65mvRDP9C6VBtA7wPOldQOnAn8vuSzXYHnMsqXc26QCR3p+zsX1am2Cv81wrjRWYTS5vSSz/6B0H/KOeeqZ2AdlmrLi2pHIv0VeI+kbc3slbKPvwK8lFnO+kGadYiqkdQ4NHPZ9FTHnnvt/HhePtHS/f07tmXZsteZMG67t9J++/iLsePWLuxpIdXukkZOZd2w5lyPDCxHzzfTqKkjvZm9IqkZeBM40MweSrMAk3PO9WagPwMt58sKO+eyMVhKoCWK9efCOZdjVtWU9HngJdAK0i63MWL8rrG09Uvja6pPvyE+Zeoz98fX5Tv2hDGxtL3Hbtvt/fxn/8Y2mzax4LnNHeyfvKX80TSsu+ehWFrSs9ik+/Lnna5RitgKX89kIh3R1HaLM8yPc26wMrD2YpVAK3ZjknRYTyvTmdk8M4tPLOmcc9UyQhU+zZYTafqB/oEwvT0QZmSSNF/Sbn2XLefcoNSZcsuJNFX48uecAg4Fts4+O865QSvqSF8k9TYiFcKG9RtSdYhP25E+qYN80vrpSf5uw2GxtKQGo2FD4pWDXz32fLf3D539B476agu3X/aXXq+Z1GDkHeRdLhUsgFY7lLNfSJos6WZJz0haL2mxpEsleSnYuYHCLATQNFtOpC2B7iBpl+jfzSVpq8t3NLMlWWSszNnAcuB8woQl+xLG4U+SdLBZ0To/OOeSDNSRSL9OSLulh32be0ivx7FmVjqb8DxJq4CfE6bVm9MH13TONZKRq9JlGmkC6Ml9nosKyoJnl64Hfzs0Mi/OuT400AKomf28ERmpwcTo9YlKOw4fMZxHllduIEk781LSqKPWyd9Jdeyn3799LG1Uc7zQ/vjq12NpSx7bEMtH07Bh3fKTNPrJG4xcIQzQEmjuSNoBuBiYbWYP9Hd+nHNZyFcn+TQKF0CjUVG3Au308nhB0jRgGsDYsWNpa2ureO5pF05NlYctR2wZS2selW7OzRef3RBLezlhv/aOeLvYoS3dF4KzHbZk69FNHHbi5vx0bmwpP4xJnfFSb5qfR39as2ZN7vNYi4F6X5kwoL1Y7cGFCqCShhNmw98FmGhmPS4hYmYzCCuF0tLSYq2trRXPf+bkc1PlY68JX46ljZ68bcKecVP3eGcsLW0V/p77V3V7v3H+ixx24pbMuXHzZCRJVfi/rrkjlpb3KnxbWxtpvrOiGaj3lQnLrhVeUiswN+Gj18xsZCYXoUABVNJQ4GbgIOBwn8DZuQEo+yr8GWxucIZQc81MIQKopCbgBuDDwNFmVlXxKWkkUtJooiRJJbWTv3JnLG3kyPiYhE/vH+8gEK/8w/Mb4tX6/56/Opa2cX735TrWL32azo0t3UqdyUuGxNO8YcnljllfVOGfqDZeVKMQARS4CjgB+BawVlLpb/9zvVXlnXMF0lmsZ6CFGMoJHBW9XkBY+bN0O7W/MuWcy1BXN6Zsh3LeIKlD0iuSfiFppyyzXIgSqJmN6+88OOf6mBkk9D7pwRhJpV0YZ0QNx11eAy4D5gGvE4Z/nw8skLSvmSV1fqlaIQKoc27gM8DSB9CVZnZAj+cyexh4uCRpnqT5wP2EhqWv15rPUoMigIrmVI1GSft89qRZsbS37RWfBGrKhG3iJ3wz3jf0+c6hsbRbH1wZS1tz+/OxtPIuSjOXTaetrY2Zy058Ky1pXaOkhiVvMHK5Y9anz0DN7CFJTwEHZnXOQRFAnXMF0fcd6UWGqwl7AHXO5UMfl0AlHQC8B/hVVuf0AOqcy40qnoH2StINwFLgIWA1oRHpPOB54MpMLoIHUOdcXphBR0dWZ3scOBE4nTB+5SXgN8A3zCze6FAjD6AlRn72g7G0t20X/xGddfj4WNor//P7WNoWux8eS/uv256NpW1aG/+rm26NpemxlOSRSM4VgIFlFEDN7FLg0kxO1gsPoM65nOjbZ6B9wQOocy4fDKw907k++pwHUOdcThjmJVDnnKueGVhnZo1IDTEoAqiamtjy0P26pdnz8QmLP37U6FjantvEJ6B7/rbLYmnbH/n5WNpNi/4WS1u7MD46KSkvPlLIDT6GdXgV3jnnqmfmJVDnnKuVB1DnnKuBYXR2burvbFTFA6hzLh+8Cp9TFm+omXTBbrHddtkUb/R54dbbYmnvOv6MWNo1C+LHbtgQn/Rl3T0PxdKSVs2Ek2Ip5esYeUOTG1i8BOqcczUxDDNvhXfOueoZdHoV3jnnamF0mlfhnXOuaobR6VX4/BmyhfjH707oljZy4S2x/Z6+a2Esbdwpk2Npi15fF0t7/ZV41SNpXaOkBqOkxqDyBqOe9nNu4DA6zavwzjlXNTOvwjvnXM068RKoc87VwKvwzjlXE8PoxKvw+dPZwahFv+uWtPCnD8d2e98/7hlL69hx/1jar2csjV/ilY2xtKR1jdI2BHmDkRt8vATqnHM1M38G6pxz1QtVeA+gzjlXAw+gzjlXM8MXlesTkt4NfB/4CCBgNnCmmS2vdOybmzo49ZxZ3dKuOf+Q2H6/3/rIWNqSL9+fKn/1NBg557KvwtcTM9IqRACVtCUwB9gIfBYw4JvAXEl7mdna/syfcy4bWZVAGxUzChFAgc8BuwAtZvY0gKSFwF+BzwP/3o95c85lJMMqfENiRlMWJ2mA44A/df0gAMxsKXAvcHy/5co5lxnD6FRnqi2FhsSMogTQPYHHE9IXAXs0OC/OuT5i6ky1pdCQmFGUKvxo4NWE9FXAqKQDJE0DpkVvNy5nebcf5gfOSmoc+n49eUzKQ6bn68EYYGUjLtRgfl/F01LPwes71t75yCt/GpNy9+GSHih5P8PMZpS8rzpm1KIoARTCQ+ByPUao6Ic5A0DSA2Z2QF9lrD8N1Hvz+yqesoBWNTOLd4OpT1UxoxZFqcK/SviLUm4UyX9lnHODW0NiRlEC6CLCM41yewD/0+C8OOfyryExoygBdBbwfkm7dCVIGgccEn1WyYzKuxTWQL03v6/iydO91RszUpFZ0mOCfJH0NuBRYD3wdcKzjUuArYG9zGxNP2bPOZczjYoZhSiBRqMGDgOeAv4LuAFYChzmwdM5V65RMaMQJVDnnMujQpRAk0h6t6RfS3pN0uuSfiNpp5THDpf0PUkvSlovaYGkD/V1ntOQNFnSzZKeifK2WNKlkrZOcaz1sO3TgKxXyltrD3lbneLY3H5fAJLaevnZx9ex7n5sbr4zSTtKujL6+a6L8jEuYb9Rkn4iaaWktZJmS5qQcMqkazRJOk/SMkkbJD0q6ZOZ30yDFKkf6FsymCjgWuBo4BxgCfAl4E5JHzCzR/os4+mcDSwHzgeeA/YFpgOTJB1sZpWGYVwH/Kgs7amM81iPM4C/lLxvT3FMnr8vgC8C25SlfYAw3jpNg8V15OM72xX4FPAgcDfw0fIdFEaHzALGA6cTugSdR/jd28fMnqtwjUsI/8cviK7zaWCmpGPM7PdZ3UjDmFnhNuArQAewa0naeMIv41kVjt2bEHBPLkkbAiwGZuXg3sYmpE2J8nxYhWMN+GZ/30MPeWuN8nd4lcfl+vvqJd/XEv7Ajy7KdwY0lfz71Chv48r2OT5Kn1SS9nbCCJ8rKpx/u+hnclFZ+h+Bhf19/7VsRa3C1zNRwHHAJuCXJce2AzcBR0galn120zOzFQnJXSW2HRqZl5zI9feVRNII4ATgNjNb1d/5Scsq124gfB8vmNnckuNeA26j8u/eEcAWwPVl6dcDEySNryK7uVDUAFrPRAF7AkvNbF3CsVsQqjF5MzF6fSLFvl+QtDF6hjVH0gf7MmM1uEFSh6RXJP0ixXPrIn5fnyB0l/l5yv3z/p2V6u13bydJW1U4diPwdFn6oui1cBMDFTWA1jNRQG/Hdn2eG5J2AC4GZptZpbHG1xOexx1OmEhlW2COpNa+zGNKrwGXEaqGhxGehR0OLJC0XS/HFer7ikwBXgZuT7Fvnr+zJJW+j95+/0YDqy2qtyccm8fvsleFbESK1DpRgOo4tqGiv+a3Ep7tnlxpfzP7x5K3d0u6lVBa+CZwaJ9kMiUzexh4uCRpnqT5wP2EhqWv93BoYb4vAEnvIgTDy6NHDb3K83fWg3q+j0J9l2kUtQRaz0QBq3o5tuvzfidpOKG1cxfgCKvcuhljZm8A/w0cmHH2MmFmDxFam3vLXyG+rxInEX6v0lbfu8n7d0bl76O3379VwKioJT/p2Lx9lxUVNYDWM1HAImB81BWq/Ng3iT+faThJQ4GbgYOAj5nZY/WcjuS/+nlRKX+5/77KTAEeNbNH6zhHnr+z3n73llvvo3wWAcOAv0s4Fgo4MVBRA2g9EwXMAoYSWkm7jh0C/ANwl5ltzDy3VZDURBh29mHgeDOreWlPSdsQ+k/+OaPsZUrSAcB76D1/uf6+SkX3syc1lj6jc+T6OyN8HztI6mrY7MrzsVT+3buD8EfvM2XpJwGPRz1piqW/+1HVsgFvI5Q8HiN0nTiOMHHAEmCrkv12Jjw//H9lx99EqGqcSghUvwY2APvl4N5+yOaBAe8v23bs6b4InZN/DPxfQp/Lz0Y/nzeBD+bgvm6I7ukThEakrxJmZl8OjCnq91WWzysIXa7ekfBZIb4zYHK0df0//EL0fmL0eRNwH/AsoRP8EUAbofr97rJztQPXlqX9a/TdnRXd8w+BTuDY/v7+avp59XcG6viidyJUc18H3gBuId7pd1z0n2B6WfoIwiiRl6Iv889Aa3/fU5S3ZVGek7bpPd0XoQRwbxSUNgGvEEoEB/X3PUX5Ow9YSGiN3xT9As4A3lnk76skj0OBFYS+n0mfF+I76+X/XlvJPqOBn0ZBcx2hI/zePZzrurK0ZkKD4TOELk0Lgcn9/f3VuvlkIs45V6OiPgN1zrl+5wHUOedq5AHUOedq5AHUOedq5AHUOedq5AHUOedq5AHUOedq5AE05yRN7WXdnMP7O399JVozp6f7Lt2W9Xde3eBV5OnsBpsTCGsklSrc5AvODSQeQIvjEStZwiTvJA2zOib6MLNxZeezks8KO3+kG1i8Cj9ASXqPpN9KejlaPna5pJnRTEal++0d7feKNi+jfF7ZPkdGS92uV1hG+hZJLSWfT4+q0++TdKekNcCvyq4xS9Kr0TnuzfmyFc6l4iXQ4mguC35mZh297P87YDVhNp2VhAXpPkbJH01JBxFm0nka+GfCI4LdgL1K9jmSMMHvHMIUclsRlhi5J1rG9vmSa95KWI3yO4QZdpC0H2GJ3IeBzxEmnzgNmB0t0/xgNT8E5/LEJxPJOUlTgZ8lfHSvmSUu+SBpDGFmoOPNrMc5GqMlNcYDLRZftK1rnwcIy9a+16IlKqLVE58CrjSzsyRNB74BnGlml5cd/0fgXYTZet6M0poJy1YsNrOP95S/svN4Fd7ljlfhi+P/EJZ56Nr+CUDBkNKNMC3aEuBfJX1O0m7lJ4tmeD8EuKGX4Pk2YD/gl1ayvo9tXkJ6Ytkhvy07fkS0z0ygsyR/AmYDH6r2h5CWpJeixwrjovdWGoQlHS7pKUnt0WfDk9LquH5XL4H26LHHw5K+UXrOHnoV3FLPfbvG8ip8cTzeQyPSRGBuaYKZSdJHgOnApcC2kpYC3zOzH0a7jSL8Ae1traVRhGD3YsJnLxEmCS5Vvt9owvyPF0ZbjKQmS7ceeb1OLHt/FuFxxZWECYLf7CGtXicDYwiTJU8HPiKp1bovODeFMB8o9P59uJzxAFp8D5KwAJmZLQGmSBKwN/Bl4GpJy8zsdsIM752EZ6M9eZUwKe72CZ9tTyjpdrts2fvV0TWuAv4z6QJZBU9JJwHfIqwVfx3xlR5vjF5vktTG5tLz6YQVDU4rTzOzmzLI2kwz2yDph4THFocQuqTdWLLPHMLkwgC9rSnkcsar8AVnZm+Y2QOlW9nnZmaPEEpXAO+L0tcB9wAnRVXtpHOvJQToE6LnlgBI2hk4GJhXIW9rCQ1IewMPleezPK+1krQ7IWiOIpS4t4u2nlwMPBH9+xJCwExKK73GKEljetgq/h6Z2Qbg99HbQ8o+fo7wzHoFIZC7gvAS6AAkaS/gcuCXhBb2ZmAqYY2aOSW7nk0IggskXUb4Rd4F2MfMugLIhYRW+N9JuprQCn8RYWmOy1Jk5yxgPnCnpGsJ1fwxhGerzWZ2bu13+paPEO7xJjO7InrOeiJhKZAYM5sj6WXgvcAcM2sDSEor8TDxRxZdxhOWYqmkp0D7MTZX4RenOI/LCQ+gA9NLhMXazgJ2JKwj9BhwTGm3ITP7i6RDCKWvKwlLzj5DSau/md0h6WhCK/uvCM8F24CvmdkLlTJiZg9JOjA6/gpCi/4K4CHgmrrvtHH2JQTpJBXXM49K+R+L3t5X9vHcqITqCsa7MbnCk/Rewh+IdYQFy/YGTok+Hm9my7pa4Lu6QJU8B51UUgKNpdWRp65frCnAWEINYAIheE40s/ayfbpKoK+a2Z31XNs1jj8DdYVnZk8QunWtJjxyeJlQys2D6wit7xCerR5e1gIPoYHtxmj7TsNy5urmJVDnnKuRl0Cdc65GHkCdc65GHkCdc65GHkCdc65GHkCdc65GHkCdc65GHkCdc65GHkCdc65GHkCdc65G/x8KKhRo5FQ0rAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins=45\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "o1,bfs,bcs,im=constplot2(fs_diff_all,fs_pseudo_all,nbins,aspect);\n",
    "\n",
    "fig.colorbar(im, ax=ax)\n",
    "\n",
    "xa,xb=0,10\n",
    "\n",
    "ax.set_aspect(1)\n",
    "ax.set_ylim([xa,xb])\n",
    "ax.set_xlim([xa,xb])\n",
    "\n",
    "ax.set_ylabel(r'F-score $\\bf T_{diff-EN}$',fontsize=fs)\n",
    "#ax.xaxis.set_label_coords(1.7,-0.18)\n",
    "ax.set_xlabel(r'F-score $\\bf T_{diff-DE}$',fontsize=fs)\n",
    "\n",
    "folderfigure='./save/figures/'\n",
    "figuresave=folderfigure+'plot_F2F_{}.svg'.format(filename0)\n",
    "print(figuresave)\n",
    "#savefig(figuresave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d0a2a55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
